Natural Language Interfaces for Database Management: Bridging the Gap Between Users and Data through Conversational AI

Abstract

Natural Language Interfaces (NLIs) for database management systems represent a transformative technology that bridges the gap between human communication patterns and structured data repositories. This article examines the evolution of database interfaces from traditional SQL syntax to conversational models powered by advanced artificial intelligence. Through a comprehensive assessment of multiple implementations across diverse organizational environments, the article demonstrates how NLIs democratize data access by enabling non-technical stakeholders to interact with complex data structures using everyday language. The technological foundations of these systems—from intent recognition and entity extraction to schema understanding and ambiguity resolution—are examined alongside their practical applications in enterprise settings. The historical trajectory reveals a significant shift from early rule-based systems with limited domain coverage to sophisticated transformer-based architectures capable of understanding context, maintaining conversational state, and handling complex queries. Current commercial implementations from major technology providers and specialized vendors are evaluated based on their capabilities, limitations, and integration approaches. The article also explores the multidimensional impact of these interfaces on organizational operations, including enhanced self-service analytics, improved decision-making processes, technical resource optimization, and measurable economic benefits. Implementation challenges related to domain-specific terminology, complex query translation, and integration with existing systems are addressed alongside effective mitigation strategies. The evidence presented establishes natural language interfaces as a fundamental advancement in human-database interaction rather than merely an incremental improvement in access technology.